# A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings

arXiv: https://arxiv.org/abs/2305.00365 · HTML: https://mlsanity.com/paper/2305.00365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 30 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [One for Many: Transfer Learning for Building HVAC Control](https://mlsanity.com/paper/2008.03625/) (2020)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [Generalized Online Transfer Learning for Climate Control in Residential Buildings](https://mlsanity.com/paper/1610.04042/) (2016)
- [B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings](https://mlsanity.com/paper/2211.03219/) (2022)

## Abstract (arXiv)

Energy optimization leveraging artificially intelligent algorithms has been proven effective. However, when buildings are commissioned, there is no historical data that could be used to train these algorithms. On-line Reinforcement Learning (RL) algorithms have shown significant promise, but their deployment carries a significant risk, because as the RL agent initially explores its action space it could cause significant discomfort to the building residents. In this paper we present ReLBOT - a new technique that uses transfer learning in conjunction with deep RL to transfer knowledge from an existing, optimized and instrumented building, to the newly commissioning smart building, to reduce the adverse impact of the reinforcement learning agent's warm-up period. We demonstrate improvements of up to 6.2 times in the duration, and up to 132 times in prediction variance, for the reinforcement learning agent's warm-up period.